The ability to measure the value of the Outside Air Temperature (“OAT”) is a key process supporting the attainment of cabin comfort in heating ventilation and air conditioning (“HVAC”) systems for vehicles that employ Automatic Temperature Control (“ATC”) algorithms. Moreover, an accurate OAT value is required by several algorithms within the HVAC electronic control module other than the ATC algorithm, and finally, OAT is required by other electronic modules within the vehicle, such as the center stack temperature display module or modules that control such features like remote starting of the vehicle.
Due to the substantial thermal noise existing in the engine compartments of automobiles, where ambient temperature sensing devices are typically located for reasons of economy, the acquisition of a timely and accurate estimate of the outside ambient temperature is difficult to achieve once the vehicle has stopped moving for even a short period of time. Difficulty arises from the fact that the temperature sensor, which is usually a type of thermistor, measures not only the desired ambient air temperature component of temperature, but it also measures additional, undesirable “noise” components of engine-generated heat that build up as a result of a lack of air flow over and around the sensing device. In addition to heat being radiated directly from the engine, note that the vehicle's cooling fan is extracting engine heat from the radiator fins and this heat floods the engine compartment. There is no ram air present when a vehicle is stopped to exhaust that hot air. But once the vehicle acquires sufficient velocity, ram air from outside of the engine compartment flows over the sensor and cools it, resulting in a series of exponentially decreasing temperature measurements. In the current state of the art of OAT filtering processes, temperature measurement algorithms do not predict the final value of such a transient, decaying data series. Rather, currently implemented algorithms maintain the last known, trusted value of the OAT until environmental conditions such as vehicle speed, coolant temperature, and engine off time indicate that the sensor is likely to be purged of its thermal noise and is thought to be providing a near accurate representation of the ambient temperature. However, several minutes must expire once vehicle speed is adequate enough to flush the sensing element of retained thermal noise before a numerical convergence can begin to materialize between the last known, trusted value of ambient temperature and the currently reported ambient sensor value. During this time, lacking an accurate OAT temperature, the cabin of the vehicle can be uncomfortable with regard to temperature, freshness of the cabin air, and humidity content of the cabin air.
Techniques that use Newton's Law of Cooling to predict the final value of an exponentially decaying real-time data series from a thermally-monitored engine compartment have not been acceptably successful at deriving accurate numeric thermal model parameters. This failure is due to high-order noise factors that are impossible to characterize in these automotive thermal systems that significantly skew the sensor's data series elements away from an ideal exponential data series, resulting in unstable data predictions that appreciably undershoot or overshoot the true ambient temperature value. A practical characterization of these factors is aggravated by the thermal exponential model's time constant dependency upon vehicle speed.
Existing linear curve fitting and final value estimation approaches which might be employed via Newton's Law of Cooling are good at establishing model parameters from complete, existing data sets that may be available after the thermal transient response has completed, but are not so good at predicting the outcome of real-time data that is necessary to improve the cabin environment.
Also, due to the real-time requirement to predict the final value of the data series, and due to the temperature offset commonly found in exponential numerical models' data series, nonlinear curve fitting approaches must be applied. For example, the value of an exponential decaying numeric converges toward a constant value, which can be, but is not necessarily a value of zero. When the constant value is non-zero, it is often referred to as the “offset”, or the “final value”. But the sensitivity of exponential models to variations in data early in the exponential transient response makes effective use of nonlinear curve fitting approaches difficult, and sometimes incapable of attaining a solution. The solution often diverges rather than converges as the process proceeds due to the deviation of the data from the ideal exponential form.
Other approaches, such as the use of a Kalman filter, weighs predictions based upon model parameters against statistical deviations of the real time data to provide “statistically filtered” data. Such an approach also does not achieve a satisfactory solution to the problem. Because of the data skewing factors previously mentioned, the model required to establish statistical parameters cannot be known ahead of time, yet it is required in the Kalman filter method to correct the current data sample and predict the next. Additionally, the thermal data from the vehicle's environment is not skewed by statistically neutral noise exhibiting an average value of zero, as is required by Kalman filter method, but rather is skewed by thermal characteristics of the system that cannot be practically or economically obtained, and these noise factors generally have a positive bias.